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Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

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  • Janczura, Joanna
  • Trueck, Stefan
  • Weron, Rafal
  • Wolff, Rodney

Abstract

An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

Suggested Citation

  • Janczura, Joanna & Trueck, Stefan & Weron, Rafal & Wolff, Rodney, 2012. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," MPRA Paper 39277, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:39277
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    References listed on IDEAS

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    More about this item

    Keywords

    Electricity spot price; Outlier treatment; Price spike; Robust modeling; Seasonality;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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